"""simple docstring"""



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE__:Union[str, Any]									=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE__:List[Any]									=			"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir    ,		"models/bert/"						)						)
        __a       =    self.transformer_dir
        shutil.copy(
            os.path.join(lowerCamelCase    ,		"src/transformers/models/bert/modeling_bert.py"						)    ,		os.path.join(self.transformer_dir    ,		"models/bert/modeling_bert.py"						)    ,		)


 def 						a__   ( self						):
        __a       =    "src/transformers"
        shutil.rmtree(self.transformer_dir						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase=None						):
        __a       =    comment + F"\nclass {class_name}(nn.Module):\n" + class_code
        if overwrite_result is not None:
               __a       =    comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
        __a       =    black.Mode(target_versions={black.TargetVersion.PYaa}    ,		line_length=119						)
        __a       =    black.format_str(lowerCamelCase    ,		mode=lowerCamelCase						)
        __a       =    os.path.join(self.transformer_dir    ,		"new_code.py"						)
        with open(lowerCamelCase    ,		"w"    ,		newline="\n"						) as f:
               f.write(lowerCamelCase						)
        if overwrite_result is None:
               self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase						)						) == 0						)
        else:
               check_copies.is_copy_consistent(f.name    ,		overwrite=lowerCamelCase						)
               with open(lowerCamelCase    ,		"r"						) as f:
                      self.assertTrue(f.read()    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"						)
        self.assertEqual(lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        # Base copy consistency
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"    ,		"BertLMPredictionHead"    ,		REFERENCE_CODE + "\n"    ,		)

        # With no empty line at the end
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"    ,		"BertLMPredictionHead"    ,		lowerCamelCase    ,		)

        # Copy consistency with rename
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"    ,		"TestModelLMPredictionHead"    ,		re.sub("Bert"    ,		"TestModel"    ,		lowerCamelCase						)    ,		)

        # Copy consistency with a really long name
        __a       =    "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
        self.check_copy_consistency(
            F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"    ,		F"{long_class_name}LMPredictionHead"    ,		re.sub("Bert"    ,		lowerCamelCase    ,		lowerCamelCase						)    ,		)

        # Copy consistency with overwrite
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"    ,		"TestModelLMPredictionHead"    ,		lowerCamelCase    ,		overwrite_result=re.sub("Bert"    ,		"TestModel"    ,		lowerCamelCase						)    ,		)






 def 						a__   ( self						):
        __a       =    check_copies.LOCALIZED_READMES["README_zh-hans.md"]

        __a       =    (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
            " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
            " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
            " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
            " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
            " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
            " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
            " method has been applied to compress GPT2 into"
            " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
            " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
            " Multilingual BERT into"
            " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
            " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
            " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
            " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
            " Luong, Quoc V. Le, Christopher D. Manning."
        )
        __a       =    (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )
        __a       =    (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
            " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
            " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
            " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
            " method has been applied to compress GPT2 into"
            " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
            " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
            " Multilingual BERT into"
            " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
            " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
            " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
            " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
            " Christopher D. Manning 发布。\n"
        )

        __a ,   __a       =    check_copies.convert_to_localized_md(
            lowerCamelCase    ,		lowerCamelCase    ,		localized_readme["format_model_list"]						)

        self.assertFalse(lowerCamelCase						)
        self.assertEqual(lowerCamelCase    ,		lowerCamelCase						)

        __a ,   __a       =    check_copies.convert_to_localized_md(
            lowerCamelCase    ,		lowerCamelCase    ,		localized_readme["format_model_list"]						)

        # Check whether the number of models is equal to README.md after conversion.
        self.assertTrue(lowerCamelCase						)

        __a       =    (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
            " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
            " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
            " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
        )
        __a       =    (
            "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
            " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )
        __a       =    (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )

        __a ,   __a       =    check_copies.convert_to_localized_md(
            lowerCamelCase    ,		lowerCamelCase    ,		localized_readme["format_model_list"]						)

        # Check if the model link is synchronized.
        self.assertEqual(lowerCamelCase    ,		lowerCamelCase						)

"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
